In medical and surgical research the hazard function is often used to estimate risk of an event across time. However the assumption of constant hazard is often not met and is not ideal when analyzing repeated events such are valve replacements, reoperations, reinterventions, multiple episodes of infection or repeated transplant rejections. I have read some information on modulated renewal theory and the Nelson estimator which involve calculating interfailure event times and segments/gaps between events within the same patient. Can anyone suggest a useful and relatively simple way of approaching this in SAS, Stata or R? Thank you!
A simple and efficient approach is to use models for discrete-time, grouped data, as described in
Prentice, R. L. & Gloeckler, L. A. Regression analysis of grouped survival data with application to breast cancer data; Biometrics, 1978, 34, 57-67
You can use marginal (eg binomial logistic regression) or random-effect models. We have implemented some models for grouped data in the package aods3 for R 'on CRAN). A case study in
https://www.researchgate.net/publication/11069589_Use_of_Akaike_information_criteria_for_model_selection_and_inference_An_application_to_assess_prevention_of_gastrointestinal_parasitism_and_respiratory_mortality_of_Guinean_goats_in_Kolda_Senegal
This approach has the advantage of accounting for competing risks when the time step is short. Also, when using a complementary log-log link and a small time step, estimates are equivalent to those obtained with continuous time (Cox) models.
Article Use of Akaike information criteria for model selection and i...
Dear David Zurakowski
Currently, repeated measures are becoming common and essential to see the progression of one disease through time. Longitudinal data analysis methods are appropriate for longitudinally recorded information, to mention some of the models depending on the type of response variable (continuous, discrete, or categorical)
Generalized Estimating Equation (GEE), Linear Mixed Model (LMM)....
i do have an on going paper on the application of LMM for repeated measure of CD4+ count of patients.
Keep in touch for more queries.
Did you find this paper?
Deals with rehospitalizations in psychiatry and compares several statistical models based on failure time analysis. Markov chains seem not well suited for your problem, due to the assumption of constant transition probabilities, as you mentioned it.
https://www.ncbi.nlm.nih.gov/pubmed/?term=frick+u+langguth+b+%26+revolving+door
Hope this helps.
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics